<title>Vector quantization of wavelet coefficients for super-high-definition image</title>

Abstract

In this paper, we describe a new coding scheme for super high definition(SHD) images using vector quantization of wavelet coefficients. This coding scheme includes hierarchical blocking and adaptive coding techniques to optimize the coding efficiency. The SHD image is decomposed into multi-resolution layers with the biorthogonal wavelet filter. The general ideas of the adaptive coding are to remove the redundancy of correlation between different layers and to attempt the minimization of perceptional error through athptive bit allocation. To achieve the high compression ratio, wavelet coefficients except for the low frequency bands are coded by using vector quantization. The binary tree codebooks are created for each resolution layer. The depth of the tree search is also adaptively controlled according to the magnitude of the input vector. To evaluate the picture quality, objective and subjective tests are performed on the reconstructed SHD images. The results of our method give better SNR and picture quality compared with the JPEG baseline coding.

<title>Classified wavelet transform coding of images using vector quantization</title>

“…Several compression techniques using the wavelet transform and different types of quantization methods have been proposed in the literature [ 7,8,9,10,11]. It decomposes an image into various multi-resolution approximations, which are accomplished by iteratively applying high-and low-pass filters to the image.…”